Factor analysis
Anna Quaglieri & Riccardo Amorati
03/09/2017
Basic factor analysis: 7 factors as the number of variables in the study design
Read in data
> all <- read.csv("../02-descriptive_data/merged_filtered_imputedMedian_likertNumber.csv")
> rownames(all) <- all$Resp.IDSeven, is the number of factors that would be present according to the study design. Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.
Likert variables
Delete some items which are context specific
> # items to be used for the FA
> usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
>
> usable_data <- all[,usable_items]
> sum(is.na(usable_data))## [1] 0> # Cronbach's alpha using consistent items across contexts
> psych::alpha(usable_data,use="pairwise.complete.obs")##
## Reliability analysis
## Call: psych::alpha(x = usable_data, use = "pairwise.complete.obs")
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd
## 0.84 0.86 0.9 0.17 5.9 0.013 4 0.33
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.84 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## converse.id1 0.83 0.85 0.89 0.16 5.5 0.014
## dream.id1 0.83 0.85 0.90 0.17 5.6 0.013
## usewell.id1 0.83 0.85 0.90 0.17 5.7 0.013
## whenever.id1 0.83 0.85 0.89 0.16 5.5 0.014
## consider.ought1 0.84 0.86 0.90 0.18 6.0 0.012
## people.ought1 0.84 0.86 0.90 0.17 5.9 0.013
## expect.ought1 0.84 0.86 0.90 0.18 6.0 0.013
## fail.ought1 0.84 0.86 0.90 0.18 6.0 0.013
## enjoy.intr1 0.83 0.85 0.89 0.17 5.7 0.013
## life.intr1 0.83 0.85 0.89 0.16 5.5 0.014
## exciting.intr1 0.83 0.85 0.89 0.17 5.6 0.013
## challenge.intr1 0.83 0.85 0.90 0.17 5.7 0.013
## job.instru1 0.83 0.85 0.89 0.17 5.6 0.014
## knowledge.instru1 0.83 0.85 0.90 0.17 5.8 0.013
## career.instru1 0.83 0.85 0.89 0.17 5.6 0.014
## money.instru1 0.83 0.85 0.90 0.17 5.8 0.013
## time.integr1 0.83 0.85 0.89 0.17 5.6 0.013
## becomelike.integr1 0.83 0.85 0.90 0.17 5.8 0.013
## meeting.integr1 0.83 0.85 0.90 0.17 5.7 0.013
## affinity.integr1 0.84 0.85 0.90 0.17 5.8 0.013
## improve.prof1 0.83 0.85 0.90 0.17 5.7 0.013
## speaking.prof1 0.83 0.85 0.89 0.17 5.6 0.013
## reading.prof1 0.84 0.85 0.89 0.17 5.8 0.013
## written.prof1 0.83 0.85 0.89 0.17 5.6 0.013
## listening.prof1 0.83 0.85 0.89 0.17 5.6 0.013
## citizen.post1 0.83 0.85 0.90 0.17 5.7 0.014
## interact.post1 0.83 0.85 0.90 0.17 5.7 0.013
## overseas.post1 0.83 0.85 0.89 0.17 5.6 0.014
## globalaccess.post1 0.83 0.85 0.89 0.17 5.5 0.014
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## converse.id1 323 0.59 0.59 0.58 0.53 4.3 0.76
## dream.id1 323 0.49 0.51 0.49 0.44 4.5 0.65
## usewell.id1 323 0.42 0.43 0.40 0.35 4.3 0.72
## whenever.id1 323 0.57 0.57 0.56 0.51 4.3 0.82
## consider.ought1 323 0.27 0.20 0.16 0.16 2.6 1.12
## people.ought1 323 0.36 0.28 0.24 0.25 3.1 1.16
## expect.ought1 323 0.29 0.21 0.18 0.20 1.9 0.92
## fail.ought1 323 0.29 0.23 0.19 0.20 2.1 0.96
## enjoy.intr1 323 0.43 0.46 0.44 0.38 4.5 0.64
## life.intr1 323 0.62 0.60 0.59 0.55 3.3 1.04
## exciting.intr1 323 0.50 0.54 0.52 0.45 4.6 0.56
## challenge.intr1 323 0.41 0.42 0.39 0.33 4.2 0.79
## job.instru1 323 0.50 0.49 0.47 0.43 3.8 0.83
## knowledge.instru1 323 0.39 0.40 0.36 0.33 4.2 0.65
## career.instru1 323 0.50 0.50 0.49 0.43 4.2 0.77
## money.instru1 323 0.40 0.39 0.36 0.33 3.2 0.77
## time.integr1 323 0.46 0.49 0.46 0.40 4.5 0.66
## becomelike.integr1 323 0.43 0.40 0.36 0.34 3.1 0.95
## meeting.integr1 323 0.42 0.45 0.43 0.37 4.6 0.57
## affinity.integr1 323 0.39 0.37 0.34 0.31 3.6 0.87
## improve.prof1 323 0.38 0.43 0.41 0.32 4.5 0.75
## speaking.prof1 323 0.44 0.50 0.50 0.40 4.7 0.53
## reading.prof1 323 0.34 0.39 0.37 0.28 4.5 0.62
## written.prof1 323 0.46 0.51 0.50 0.41 4.6 0.58
## listening.prof1 323 0.44 0.49 0.49 0.39 4.5 0.63
## citizen.post1 323 0.50 0.48 0.45 0.42 3.8 0.89
## interact.post1 323 0.44 0.47 0.44 0.38 4.4 0.62
## overseas.post1 323 0.51 0.55 0.53 0.47 4.6 0.58
## globalaccess.post1 323 0.53 0.56 0.54 0.48 4.3 0.67
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## converse.id1 0.00 0.03 0.10 0.41 0.47 0
## dream.id1 0.00 0.00 0.07 0.36 0.56 0
## usewell.id1 0.00 0.02 0.11 0.46 0.42 0
## whenever.id1 0.00 0.03 0.12 0.37 0.47 0
## consider.ought1 0.14 0.40 0.21 0.19 0.06 0
## people.ought1 0.09 0.27 0.25 0.28 0.11 0
## expect.ought1 0.39 0.44 0.09 0.07 0.01 0
## fail.ought1 0.27 0.46 0.16 0.10 0.01 0
## enjoy.intr1 0.00 0.01 0.06 0.40 0.54 0
## life.intr1 0.02 0.24 0.25 0.36 0.12 0
## exciting.intr1 0.00 0.01 0.02 0.37 0.61 0
## challenge.intr1 0.00 0.03 0.12 0.48 0.36 0
## job.instru1 0.00 0.04 0.32 0.41 0.23 0
## knowledge.instru1 0.00 0.01 0.09 0.59 0.32 0
## career.instru1 0.00 0.00 0.20 0.41 0.39 0
## money.instru1 0.01 0.12 0.55 0.26 0.06 0
## time.integr1 0.00 0.01 0.07 0.29 0.63 0
## becomelike.integr1 0.03 0.23 0.47 0.18 0.10 0
## meeting.integr1 0.00 0.00 0.03 0.37 0.59 0
## affinity.integr1 0.01 0.07 0.36 0.39 0.17 0
## improve.prof1 0.01 0.02 0.03 0.34 0.59 0
## speaking.prof1 0.00 0.01 0.00 0.28 0.71 0
## reading.prof1 0.00 0.02 0.02 0.38 0.59 0
## written.prof1 0.00 0.01 0.02 0.36 0.62 0
## listening.prof1 0.00 0.01 0.04 0.38 0.57 0
## citizen.post1 0.01 0.07 0.23 0.46 0.23 0
## interact.post1 0.00 0.00 0.06 0.43 0.50 0
## overseas.post1 0.00 0.01 0.02 0.34 0.63 0
## globalaccess.post1 0.00 0.01 0.06 0.49 0.43 0> fact <- 7
> loading_cutoff <- 0.2
> fa_basic <- fa(usable_data,fact)## Loading required namespace: GPArotation> fa_basic## Factor Analysis using method = minres
## Call: fa(r = usable_data, nfactors = fact)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR3 MR4 MR7 MR5 MR6 MR1 h2 u2 com
## converse.id1 0.09 0.10 0.04 0.35 0.14 0.26 0.08 0.40 0.60 2.7
## dream.id1 0.17 0.07 0.16 0.19 0.17 -0.04 0.38 0.39 0.61 3.0
## usewell.id1 -0.02 0.03 0.18 0.13 0.17 0.06 0.30 0.28 0.72 3.0
## whenever.id1 -0.03 0.12 0.18 0.11 0.28 0.21 0.25 0.44 0.56 4.5
## consider.ought1 0.10 0.51 0.06 0.14 -0.09 -0.13 -0.18 0.38 0.62 1.8
## people.ought1 -0.04 0.48 0.24 0.09 0.01 -0.14 0.08 0.31 0.69 1.8
## expect.ought1 0.03 0.80 0.01 0.04 -0.03 -0.09 -0.06 0.68 0.32 1.0
## fail.ought1 -0.02 0.73 -0.10 -0.12 -0.01 0.16 0.12 0.53 0.47 1.3
## enjoy.intr1 0.02 -0.12 0.01 0.01 0.81 -0.06 0.01 0.66 0.34 1.1
## life.intr1 -0.11 0.20 0.09 0.14 0.55 0.18 0.00 0.55 0.45 1.8
## exciting.intr1 0.25 0.02 -0.02 0.16 0.33 0.07 0.14 0.35 0.65 2.9
## challenge.intr1 0.21 0.00 -0.05 -0.03 0.45 0.07 -0.14 0.29 0.71 1.7
## job.instru1 0.00 0.01 0.83 0.00 -0.03 0.01 -0.03 0.68 0.32 1.0
## knowledge.instru1 0.12 0.13 -0.02 -0.02 0.15 0.33 -0.22 0.24 0.76 3.0
## career.instru1 0.00 -0.06 0.65 -0.01 0.03 0.12 0.14 0.57 0.43 1.2
## money.instru1 0.02 0.03 0.59 -0.08 0.08 0.02 -0.13 0.36 0.64 1.2
## time.integr1 0.06 -0.05 -0.08 0.57 0.06 0.12 0.19 0.46 0.54 1.4
## becomelike.integr1 0.01 0.05 0.03 0.49 0.12 0.04 -0.30 0.37 0.63 1.8
## meeting.integr1 0.05 -0.11 -0.01 0.51 0.11 0.04 0.17 0.40 0.60 1.5
## affinity.integr1 -0.09 0.04 0.01 0.74 -0.03 -0.04 -0.06 0.52 0.48 1.1
## improve.prof1 0.70 -0.03 -0.07 0.08 -0.04 0.07 -0.06 0.51 0.49 1.1
## speaking.prof1 0.80 -0.06 0.08 0.09 -0.10 0.00 0.01 0.65 0.35 1.1
## reading.prof1 0.69 0.01 -0.03 -0.13 0.11 -0.04 0.01 0.51 0.49 1.1
## written.prof1 0.76 0.05 0.08 -0.03 0.05 -0.06 0.09 0.61 0.39 1.1
## listening.prof1 0.85 0.05 -0.06 -0.05 0.03 0.05 -0.05 0.76 0.24 1.0
## citizen.post1 0.04 0.11 0.16 0.03 0.04 0.49 -0.28 0.42 0.58 2.0
## interact.post1 -0.02 -0.12 0.16 0.05 0.10 0.40 0.13 0.36 0.64 2.0
## overseas.post1 0.26 -0.02 0.10 0.12 -0.11 0.46 0.06 0.41 0.59 2.0
## globalaccess.post1 0.00 -0.11 0.15 0.05 0.05 0.62 0.05 0.57 0.43 1.2
##
## MR2 MR3 MR4 MR7 MR5 MR6 MR1
## SS loadings 3.30 1.86 2.00 1.95 1.89 1.79 0.85
## Proportion Var 0.11 0.06 0.07 0.07 0.07 0.06 0.03
## Cumulative Var 0.11 0.18 0.25 0.31 0.38 0.44 0.47
## Proportion Explained 0.24 0.14 0.15 0.14 0.14 0.13 0.06
## Cumulative Proportion 0.24 0.38 0.53 0.67 0.81 0.94 1.00
##
## With factor correlations of
## MR2 MR3 MR4 MR7 MR5 MR6 MR1
## MR2 1.00 0.08 0.10 0.08 0.20 0.24 0.04
## MR3 0.08 1.00 0.07 0.08 -0.03 0.02 -0.15
## MR4 0.10 0.07 1.00 0.26 0.27 0.42 0.14
## MR7 0.08 0.08 0.26 1.00 0.33 0.28 0.12
## MR5 0.20 -0.03 0.27 0.33 1.00 0.36 0.20
## MR6 0.24 0.02 0.42 0.28 0.36 1.00 0.14
## MR1 0.04 -0.15 0.14 0.12 0.20 0.14 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 7 factors are sufficient.
##
## The degrees of freedom for the null model are 406 and the objective function was 10.52 with Chi Square of 3278.37
## The degrees of freedom for the model are 224 and the objective function was 1.23
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 323 with the empirical chi square 224.23 with prob < 0.48
## The total number of observations was 323 with Likelihood Chi Square = 376.07 with prob < 8.2e-10
##
## Tucker Lewis Index of factoring reliability = 0.902
## RMSEA index = 0.049 and the 90 % confidence intervals are 0.038 0.054
## BIC = -918.12
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## MR2 MR3 MR4 MR7 MR5
## Correlation of (regression) scores with factors 0.95 0.90 0.90 0.88 0.89
## Multiple R square of scores with factors 0.90 0.81 0.82 0.77 0.79
## Minimum correlation of possible factor scores 0.80 0.62 0.63 0.53 0.58
## MR6 MR1
## Correlation of (regression) scores with factors 0.86 0.75
## Multiple R square of scores with factors 0.74 0.57
## Minimum correlation of possible factor scores 0.49 0.13> # plot loadings
> loadings_basic <- fa_basic$loadings
> class(loadings_basic)<-"matrix"
> colnames(loadings_basic)<-paste("F",1:fact,sep="")
> loadings_basic<-as.data.frame(loadings_basic)
> loadings_basic<-round(loadings_basic,2)
> loadings_basic$D <- rownames(loadings_basic)
> a1 <- loadings_basic
>
> a1 <- melt(a1,id.vars=c("D"))
> a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
> a1$value[abs(a1$value) < loading_cutoff] <- 0
> a1 <- a1[a1$value!=0,]
> a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
>
> ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
> # Table of the factors
> loadings_basic$D <- NULL
> loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
> for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
>
> loadings_basic[loadings_basic=="0"] <- ""
> loading_fact_reduced <- loadings_basic
> loading_fact_reduced## F1 F2 F3 F4 F5 F6 F7
## converse.id1 0.35 0.26
## dream.id1 0.38
## usewell.id1 0.3
## whenever.id1 0.28 0.21 0.25
## consider.ought1 0.51
## people.ought1 0.48 0.24
## expect.ought1 0.8
## fail.ought1 0.73
## enjoy.intr1 0.81
## life.intr1 0.2 0.55
## exciting.intr1 0.25 0.33
## challenge.intr1 0.21 0.45
## job.instru1 0.83
## knowledge.instru1 0.33 -0.22
## career.instru1 0.65
## money.instru1 0.59
## time.integr1 0.57
## becomelike.integr1 0.49 -0.3
## meeting.integr1 0.51
## affinity.integr1 0.74
## improve.prof1 0.7
## speaking.prof1 0.8
## reading.prof1 0.69
## written.prof1 0.76
## listening.prof1 0.85
## citizen.post1 0.49 -0.28
## interact.post1 0.4
## overseas.post1 0.26 0.46
## globalaccess.post1 0.62> # predict values per samples
> pred_basic <- as.data.frame(predict(fa_basic,usable_data))
> names(pred_basic) <- paste("Factor",1:fact,sep = "")
>
> factors <- names(pred_basic)
> match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
> all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
> corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))
> # Plot loadings by context
> all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
>
> library(ggplot2)
> ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
> # 7 * 12 rows removedBasic factor analysis: 6 factors
Using very relaxed cutoff of 0.2 to get rid of not important variables in each factor.
> # items to be used for the FA
> usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
>
> usable_data <- all[,usable_items]
>
> # From a statisticak point of view
> fap <- fa.parallel(usable_data)
## Parallel analysis suggests that the number of factors = 6 and the number of components = 4> fact <- 6
> loading_cutoff <- 0.2
> fa_basic <- fa(usable_data,fact)
>
> fa_basic## Factor Analysis using method = minres
## Call: fa(r = usable_data, nfactors = fact)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR4 MR3 MR5 MR1 MR6 h2 u2 com
## converse.id1 0.10 0.12 0.06 0.38 0.20 0.13 0.40 0.60 2.3
## dream.id1 0.18 0.27 0.04 0.22 0.25 -0.27 0.37 0.63 4.7
## usewell.id1 -0.01 0.29 -0.01 0.16 0.25 -0.15 0.27 0.73 3.2
## whenever.id1 -0.01 0.31 0.06 0.14 0.39 -0.01 0.43 0.57 2.3
## consider.ought1 0.08 -0.04 0.57 0.10 -0.13 0.05 0.37 0.63 1.3
## people.ought1 -0.05 0.22 0.51 0.06 0.02 -0.12 0.31 0.69 1.6
## expect.ought1 0.03 -0.03 0.83 0.01 -0.01 0.01 0.70 0.30 1.0
## fail.ought1 0.01 0.01 0.62 -0.09 0.10 0.05 0.39 0.61 1.1
## enjoy.intr1 0.02 -0.05 -0.12 0.01 0.73 -0.02 0.55 0.45 1.1
## life.intr1 -0.11 0.10 0.18 0.13 0.61 0.15 0.56 0.44 1.5
## exciting.intr1 0.25 0.02 -0.01 0.18 0.38 -0.03 0.35 0.65 2.3
## challenge.intr1 0.21 -0.10 0.00 -0.05 0.43 0.14 0.26 0.74 1.9
## job.instru1 -0.01 0.78 0.06 -0.01 -0.07 0.05 0.58 0.42 1.0
## knowledge.instru1 0.13 -0.02 0.11 -0.02 0.17 0.38 0.24 0.76 1.9
## career.instru1 0.00 0.76 -0.06 -0.01 0.04 -0.02 0.59 0.41 1.0
## money.instru1 0.01 0.52 0.07 -0.10 0.02 0.11 0.30 0.70 1.2
## time.integr1 0.07 0.00 -0.08 0.63 0.10 -0.03 0.47 0.53 1.1
## becomelike.integr1 -0.01 -0.08 0.11 0.43 0.04 0.22 0.27 0.73 1.7
## meeting.integr1 0.05 0.03 -0.12 0.56 0.13 -0.08 0.41 0.59 1.3
## affinity.integr1 -0.11 -0.05 0.09 0.72 -0.07 0.03 0.49 0.51 1.1
## improve.prof1 0.70 -0.08 -0.03 0.09 -0.06 0.09 0.51 0.49 1.1
## speaking.prof1 0.80 0.08 -0.04 0.10 -0.12 -0.01 0.65 0.35 1.1
## reading.prof1 0.70 -0.04 0.01 -0.14 0.11 -0.04 0.51 0.49 1.1
## written.prof1 0.76 0.09 0.05 -0.03 0.05 -0.10 0.61 0.39 1.1
## listening.prof1 0.86 -0.07 0.05 -0.05 0.03 0.07 0.76 0.24 1.0
## citizen.post1 0.05 0.21 0.09 0.03 0.05 0.52 0.43 0.57 1.4
## interact.post1 0.01 0.32 -0.19 0.10 0.19 0.17 0.34 0.66 3.2
## overseas.post1 0.29 0.27 -0.08 0.17 -0.02 0.25 0.36 0.64 3.8
## globalaccess.post1 0.05 0.36 -0.19 0.11 0.16 0.34 0.50 0.50 3.2
##
## MR2 MR4 MR3 MR5 MR1 MR6
## SS loadings 3.35 2.47 1.88 2.11 2.15 1.03
## Proportion Var 0.12 0.09 0.06 0.07 0.07 0.04
## Cumulative Var 0.12 0.20 0.27 0.34 0.41 0.45
## Proportion Explained 0.26 0.19 0.14 0.16 0.17 0.08
## Cumulative Proportion 0.26 0.45 0.59 0.76 0.92 1.00
##
## With factor correlations of
## MR2 MR4 MR3 MR5 MR1 MR6
## MR2 1.00 0.13 0.05 0.11 0.22 0.16
## MR4 0.13 1.00 0.00 0.33 0.37 0.24
## MR3 0.05 0.00 1.00 0.03 -0.05 0.11
## MR5 0.11 0.33 0.03 1.00 0.39 0.16
## MR1 0.22 0.37 -0.05 0.39 1.00 0.16
## MR6 0.16 0.24 0.11 0.16 0.16 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 406 and the objective function was 10.52 with Chi Square of 3278.37
## The degrees of freedom for the model are 247 and the objective function was 1.46
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 323 with the empirical chi square 293.67 with prob < 0.022
## The total number of observations was 323 with Likelihood Chi Square = 448.4 with prob < 7.6e-14
##
## Tucker Lewis Index of factoring reliability = 0.883
## RMSEA index = 0.053 and the 90 % confidence intervals are 0.043 0.058
## BIC = -978.68
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## MR2 MR4 MR3 MR5 MR1
## Correlation of (regression) scores with factors 0.95 0.91 0.90 0.88 0.89
## Multiple R square of scores with factors 0.90 0.82 0.81 0.78 0.79
## Minimum correlation of possible factor scores 0.80 0.65 0.63 0.55 0.57
## MR6
## Correlation of (regression) scores with factors 0.78
## Multiple R square of scores with factors 0.61
## Minimum correlation of possible factor scores 0.22> # plot loadings
> loadings_basic <- fa_basic$loadings
> class(loadings_basic)<-"matrix"
> colnames(loadings_basic)<-paste("F",1:fact,sep="")
> loadings_basic<-as.data.frame(loadings_basic)
> loadings_basic<-round(loadings_basic,2)
> loadings_basic$D <- rownames(loadings_basic)
> a1 <- loadings_basic
>
> a1 <- melt(a1,id.vars=c("D"))
> a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
> a1$value[abs(a1$value) < loading_cutoff] <- 0
> a1 <- a1[a1$value!=0,]
> a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
>
> ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
> # Table of the factors
> loadings_basic$D <- NULL
> loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
> for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
>
> loadings_basic[loadings_basic=="0"] <- ""
> loading_fact_reduced <- loadings_basic
> loading_fact_reduced## F1 F2 F3 F4 F5 F6
## converse.id1 0.38 0.2
## dream.id1 0.27 0.22 0.25 -0.27
## usewell.id1 0.29 0.25
## whenever.id1 0.31 0.39
## consider.ought1 0.57
## people.ought1 0.22 0.51
## expect.ought1 0.83
## fail.ought1 0.62
## enjoy.intr1 0.73
## life.intr1 0.61
## exciting.intr1 0.25 0.38
## challenge.intr1 0.21 0.43
## job.instru1 0.78
## knowledge.instru1 0.38
## career.instru1 0.76
## money.instru1 0.52
## time.integr1 0.63
## becomelike.integr1 0.43 0.22
## meeting.integr1 0.56
## affinity.integr1 0.72
## improve.prof1 0.7
## speaking.prof1 0.8
## reading.prof1 0.7
## written.prof1 0.76
## listening.prof1 0.86
## citizen.post1 0.21 0.52
## interact.post1 0.32
## overseas.post1 0.29 0.27 0.25
## globalaccess.post1 0.36 0.34> # predict values per samples
> pred_basic <- as.data.frame(predict(fa_basic,usable_data))
> names(pred_basic) <- paste("Factor",1:fact,sep = "")
>
> factors <- names(pred_basic)
> match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
> all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
> corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))
> # Plot loadings by context
> all_complete_basic <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
>
> library(ggplot2)
> ggplot(all_complete_basic)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
> # 7 * 12 rows removed
>
> # error bar
> sum_stat <- all_complete_basic %>% group_by(Context,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(Context[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI")
> ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales="free_y") +
+ geom_point() + ggtitle("Mean +- 95% CI")
> kable(sum_stat)| Context | variable | meanFac | stdFac | nObs | seMean | CI95 |
|---|---|---|---|---|---|---|
| English in Germany | Factor1 | -0.7673653 | 1.2968739 | 70 | 0.1550061 | 0.3038119 |
| English in Germany | Factor2 | 0.2464654 | 0.8811501 | 70 | 0.1053176 | 0.2064224 |
| English in Germany | Factor3 | -0.4025772 | 0.6874385 | 70 | 0.0821646 | 0.1610427 |
| English in Germany | Factor4 | 0.3070734 | 0.9509279 | 70 | 0.1136576 | 0.2227689 |
| English in Germany | Factor5 | 0.0672303 | 0.8805028 | 70 | 0.1052402 | 0.2062708 |
| English in Germany | Factor6 | -0.3916784 | 1.0244232 | 70 | 0.1224420 | 0.2399863 |
| English in Italy | Factor1 | 0.1674403 | 0.7825545 | 91 | 0.0820340 | 0.1607866 |
| English in Italy | Factor2 | 0.5194919 | 0.7674261 | 91 | 0.0804481 | 0.1576783 |
| English in Italy | Factor3 | 0.0054434 | 0.9238201 | 91 | 0.0968427 | 0.1898116 |
| English in Italy | Factor4 | 0.0311704 | 0.9431596 | 91 | 0.0988700 | 0.1937852 |
| English in Italy | Factor5 | 0.4084333 | 0.8766512 | 91 | 0.0918980 | 0.1801201 |
| English in Italy | Factor6 | 0.3768171 | 0.8018947 | 91 | 0.0840614 | 0.1647604 |
| German in Australia | Factor1 | 0.2302922 | 0.7943686 | 88 | 0.0846800 | 0.1659728 |
| German in Australia | Factor2 | -0.2004232 | 1.0176085 | 88 | 0.1084774 | 0.2126158 |
| German in Australia | Factor3 | 0.0300804 | 1.1010277 | 88 | 0.1173699 | 0.2300451 |
| German in Australia | Factor4 | -0.2897234 | 1.1273569 | 88 | 0.1201766 | 0.2355462 |
| German in Australia | Factor5 | -0.3245639 | 1.0203688 | 88 | 0.1087717 | 0.2131925 |
| German in Australia | Factor6 | -0.0289222 | 1.0127781 | 88 | 0.1079625 | 0.2116065 |
| Italian in Australia | Factor1 | 0.2461188 | 0.7676304 | 74 | 0.0892352 | 0.1749010 |
| Italian in Australia | Factor2 | -0.6336365 | 0.9310097 | 74 | 0.1082277 | 0.2121263 |
| Italian in Australia | Factor3 | 0.3383511 | 1.0930484 | 74 | 0.1270643 | 0.2490460 |
| Italian in Australia | Factor4 | 0.0157300 | 0.8670613 | 74 | 0.1007938 | 0.1975559 |
| Italian in Australia | Factor5 | -0.1798909 | 1.0572445 | 74 | 0.1229022 | 0.2408883 |
| Italian in Australia | Factor6 | -0.0584826 | 1.0369273 | 74 | 0.1205404 | 0.2362591 |
Factor analysis correcting for context
> # items to be used for the FA
> usable_items <- likert_variables1[!(likert_variables1 %in% c("necessity1","educated1","reconnect.comm1", "speakersmelb.comm1", "comecloser.comm1"))]
>
> usable_data <- all[,c(usable_items,"Context")]
> dat_onlyItems <- usable_data[,usable_items]
>
> # get residuals after regressing for context
> get_residuals <- function(item,pred = dat$Context){
+ mod <- lm(item ~ pred)
+ return(mod$residuals)
+ }
>
>
> applygetRes <- apply(as.matrix(dat_onlyItems),2,get_residuals,
+ pred=usable_data$Context)
>
> # Factanal
> # From a statisticak point of view
> fap <- fa.parallel(applygetRes)
## Parallel analysis suggests that the number of factors = 6 and the number of components = 6> fact <- 6
> loading_cutoff <- 0.2
> fa_basic <- fa(applygetRes,fact)
>
> fa_basic## Factor Analysis using method = minres
## Call: fa(r = applygetRes, nfactors = fact)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR3 MR4 MR5 MR6 h2 u2 com
## converse.id1 0.06 0.45 0.04 0.12 0.14 0.09 0.39 0.61 1.5
## dream.id1 0.16 0.29 0.04 0.23 0.21 -0.28 0.37 0.63 4.4
## usewell.id1 0.08 0.15 0.04 0.21 0.25 -0.10 0.22 0.78 3.4
## whenever.id1 0.01 0.23 0.09 0.21 0.34 -0.02 0.36 0.64 2.7
## consider.ought1 0.08 0.02 0.56 0.04 -0.10 0.10 0.36 0.64 1.2
## people.ought1 0.01 0.03 0.53 0.17 -0.01 -0.09 0.32 0.68 1.3
## expect.ought1 0.00 0.02 0.82 -0.02 -0.01 0.00 0.67 0.33 1.0
## fail.ought1 0.00 -0.03 0.62 -0.04 0.06 0.01 0.39 0.61 1.0
## enjoy.intr1 0.02 0.00 -0.11 -0.02 0.75 -0.02 0.57 0.43 1.0
## life.intr1 -0.09 0.18 0.18 0.07 0.55 0.12 0.51 0.49 1.7
## exciting.intr1 0.17 0.21 -0.02 0.08 0.39 -0.07 0.37 0.63 2.1
## challenge.intr1 0.17 -0.08 -0.01 -0.05 0.46 0.15 0.28 0.72 1.6
## job.instru1 0.00 -0.04 0.06 0.78 -0.05 0.05 0.60 0.40 1.0
## knowledge.instru1 0.03 -0.02 0.06 0.06 0.21 0.38 0.25 0.75 1.7
## career.instru1 -0.01 0.03 -0.07 0.72 0.03 -0.04 0.53 0.47 1.0
## money.instru1 -0.01 -0.14 0.07 0.54 0.07 0.12 0.33 0.67 1.3
## time.integr1 0.04 0.64 -0.05 -0.01 0.09 -0.02 0.47 0.53 1.1
## becomelike.integr1 0.00 0.38 0.11 -0.06 0.05 0.25 0.26 0.74 2.1
## meeting.integr1 0.07 0.55 -0.09 -0.01 0.10 -0.06 0.38 0.62 1.2
## affinity.integr1 -0.09 0.67 0.11 -0.05 -0.06 0.07 0.44 0.56 1.1
## improve.prof1 0.64 0.14 -0.06 -0.04 -0.07 0.06 0.44 0.56 1.2
## speaking.prof1 0.73 0.13 -0.07 0.12 -0.13 -0.04 0.60 0.40 1.2
## reading.prof1 0.69 -0.13 0.01 -0.08 0.10 -0.01 0.48 0.52 1.1
## written.prof1 0.78 -0.05 0.08 0.04 0.05 -0.05 0.62 0.38 1.1
## listening.prof1 0.83 -0.04 0.03 -0.06 0.04 0.06 0.69 0.31 1.0
## citizen.post1 0.05 0.06 0.07 0.13 0.00 0.54 0.40 0.60 1.2
## interact.post1 0.09 0.21 -0.17 0.14 0.12 0.19 0.25 0.75 4.9
## overseas.post1 0.29 0.20 -0.06 0.18 -0.03 0.27 0.35 0.65 3.6
## globalaccess.post1 0.05 0.15 -0.19 0.29 0.15 0.35 0.46 0.54 3.5
##
## MR2 MR1 MR3 MR4 MR5 MR6
## SS loadings 3.08 2.28 1.87 2.07 1.99 1.07
## Proportion Var 0.11 0.08 0.06 0.07 0.07 0.04
## Cumulative Var 0.11 0.19 0.25 0.32 0.39 0.43
## Proportion Explained 0.25 0.18 0.15 0.17 0.16 0.09
## Cumulative Proportion 0.25 0.43 0.59 0.75 0.91 1.00
##
## With factor correlations of
## MR2 MR1 MR3 MR4 MR5 MR6
## MR2 1.00 0.20 -0.04 0.23 0.27 0.11
## MR1 0.20 1.00 0.05 0.35 0.40 0.20
## MR3 -0.04 0.05 1.00 0.08 -0.04 0.12
## MR4 0.23 0.35 0.08 1.00 0.30 0.27
## MR5 0.27 0.40 -0.04 0.30 1.00 0.16
## MR6 0.11 0.20 0.12 0.27 0.16 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 6 factors are sufficient.
##
## The degrees of freedom for the null model are 406 and the objective function was 9.64 with Chi Square of 3002.9
## The degrees of freedom for the model are 247 and the objective function was 1.41
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 323 with the empirical chi square 304.9 with prob < 0.007
## The total number of observations was 323 with Likelihood Chi Square = 433.55 with prob < 2.2e-12
##
## Tucker Lewis Index of factoring reliability = 0.88
## RMSEA index = 0.051 and the 90 % confidence intervals are 0.041 0.056
## BIC = -993.53
## Fit based upon off diagonal values = 0.98
## Measures of factor score adequacy
## MR2 MR1 MR3 MR4 MR5
## Correlation of (regression) scores with factors 0.94 0.88 0.9 0.89 0.88
## Multiple R square of scores with factors 0.88 0.78 0.8 0.80 0.77
## Minimum correlation of possible factor scores 0.76 0.56 0.6 0.60 0.54
## MR6
## Correlation of (regression) scores with factors 0.78
## Multiple R square of scores with factors 0.61
## Minimum correlation of possible factor scores 0.22> # plot loadings
> loadings_basic <- fa_basic$loadings
> class(loadings_basic)<-"matrix"
> colnames(loadings_basic)<-paste("F",1:fact,sep="")
> loadings_basic<-as.data.frame(loadings_basic)
> loadings_basic<-round(loadings_basic,2)
> loadings_basic$D <- rownames(loadings_basic)
> a1 <- loadings_basic
>
> a1 <- melt(a1,id.vars=c("D"))
> a1$inv <- ifelse(a1$value < 0 ,"neg","pos")
> a1$value[abs(a1$value) < loading_cutoff] <- 0
> a1 <- a1[a1$value!=0,]
> a1 <- a1 %>% separate(D,into = c("Variable","Item"),remove=FALSE,sep="[.]")
>
> ggplot(a1)+geom_bar(aes(x=reorder(D, value) ,y=value,fill=Item),stat="identity")+facet_wrap(~variable,ncol = 2,scales = "free_y")+coord_flip() + geom_hline(yintercept = c(-0.3,0.3),linetype="dotted",colour="dark red")
> # Table of the factors
> loadings_basic$D <- NULL
> loadings_basic[abs(loadings_basic) < loading_cutoff] <- 0
> for(i in 1:ncol(loadings_basic)){loadings_basic[,i] <- as.character(loadings_basic[,i])}
>
> loadings_basic[loadings_basic=="0"] <- ""
> loading_fact_reduced <- loadings_basic
> loading_fact_reduced## F1 F2 F3 F4 F5 F6
## converse.id1 0.45
## dream.id1 0.29 0.23 0.21 -0.28
## usewell.id1 0.21 0.25
## whenever.id1 0.23 0.21 0.34
## consider.ought1 0.56
## people.ought1 0.53
## expect.ought1 0.82
## fail.ought1 0.62
## enjoy.intr1 0.75
## life.intr1 0.55
## exciting.intr1 0.21 0.39
## challenge.intr1 0.46
## job.instru1 0.78
## knowledge.instru1 0.21 0.38
## career.instru1 0.72
## money.instru1 0.54
## time.integr1 0.64
## becomelike.integr1 0.38 0.25
## meeting.integr1 0.55
## affinity.integr1 0.67
## improve.prof1 0.64
## speaking.prof1 0.73
## reading.prof1 0.69
## written.prof1 0.78
## listening.prof1 0.83
## citizen.post1 0.54
## interact.post1 0.21
## overseas.post1 0.29 0.2 0.27
## globalaccess.post1 0.29 0.35> # predict values per samples
> pred_basic <- as.data.frame(predict(fa_basic,dat_onlyItems))
> names(pred_basic) <- paste("Factor",1:fact,sep = "")
>
> factors <- names(pred_basic)
> match_initial_data <- match(all$Resp.ID,rownames(pred_basic))
> all_complete_basic <- cbind(all,scale(pred_basic[match_initial_data,]))
> corrplot(cor(all_complete_basic[,usable_items],all_complete_basic[,factors],use = "pair"))
> # Plot loadings by context
> all_complete_melt <- melt(all_complete_basic,id.vars = "Context",measure.vars = factors)
>
> library(ggplot2)
> ggplot(all_complete_melt)+geom_boxplot(aes(x=Context,y=value,color=Context))+facet_wrap(~variable)+coord_flip()+guides(color=F)
> # error bar
> sum_stat <- all_complete_melt %>% group_by(Context,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(Context[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> ggplot(sum_stat,aes(x=Context,y=meanFac,colour=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~variable,scales="free_y") + geom_point() +theme(axis.text.x = element_text(angle = 45, hjust = 1))+ ggtitle("Mean +- 95% CI")
> ggplot(sum_stat,aes(x=variable,y=meanFac,colour=variable)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2) + facet_wrap(~Context,scales="free_y") +
+ geom_point() + ggtitle("Mean +- 95% CI")
> kable(sum_stat)| Context | variable | meanFac | stdFac | nObs | seMean | CI95 |
|---|---|---|---|---|---|---|
| English in Germany | Factor1 | -0.7262823 | 1.3027239 | 70 | 0.1557053 | 0.3051824 |
| English in Germany | Factor2 | 0.2482873 | 0.9717385 | 70 | 0.1161450 | 0.2276441 |
| English in Germany | Factor3 | -0.3577921 | 0.6873827 | 70 | 0.0821579 | 0.1610296 |
| English in Germany | Factor4 | 0.1984720 | 0.9209535 | 70 | 0.1100750 | 0.2157470 |
| English in Germany | Factor5 | 0.0623044 | 0.8805362 | 70 | 0.1052442 | 0.2062786 |
| English in Germany | Factor6 | -0.2942834 | 1.0170296 | 70 | 0.1215583 | 0.2382543 |
| English in Italy | Factor1 | 0.1843902 | 0.7928113 | 91 | 0.0831092 | 0.1628940 |
| English in Italy | Factor2 | 0.1305766 | 0.9120258 | 91 | 0.0956063 | 0.1873883 |
| English in Italy | Factor3 | 0.0131736 | 0.9281411 | 91 | 0.0972956 | 0.1906994 |
| English in Italy | Factor4 | 0.4605985 | 0.8027893 | 91 | 0.0841552 | 0.1649442 |
| English in Italy | Factor5 | 0.3597625 | 0.8859079 | 91 | 0.0928684 | 0.1820220 |
| English in Italy | Factor6 | 0.3618400 | 0.8155681 | 91 | 0.0854948 | 0.1675697 |
| German in Australia | Factor1 | 0.2071940 | 0.8075060 | 88 | 0.0860804 | 0.1687176 |
| German in Australia | Factor2 | -0.3001195 | 1.1310092 | 88 | 0.1205660 | 0.2363093 |
| German in Australia | Factor3 | 0.0073308 | 1.1099903 | 88 | 0.1183254 | 0.2319177 |
| German in Australia | Factor4 | -0.1437635 | 1.0280115 | 88 | 0.1095864 | 0.2147893 |
| German in Australia | Factor5 | -0.2915304 | 1.0361113 | 88 | 0.1104498 | 0.2164817 |
| German in Australia | Factor6 | -0.0633571 | 1.0120855 | 88 | 0.1078887 | 0.2114618 |
| Italian in Australia | Factor1 | 0.2138808 | 0.7800659 | 74 | 0.0906808 | 0.1777344 |
| Italian in Australia | Factor2 | -0.0385415 | 0.8820267 | 74 | 0.1025335 | 0.2009657 |
| Italian in Australia | Factor3 | 0.3135343 | 1.1005654 | 74 | 0.1279381 | 0.2507587 |
| Italian in Australia | Factor4 | -0.5831934 | 0.9398504 | 74 | 0.1092554 | 0.2141406 |
| Italian in Australia | Factor5 | -0.1546625 | 1.0673902 | 74 | 0.1240816 | 0.2431999 |
| Italian in Australia | Factor6 | -0.0912455 | 1.0643970 | 74 | 0.1237336 | 0.2425179 |
Demographics
> demographics_var <- c("Age","Gender","L1","speak.other.L2","study.other.L2","origins","year.studyL2","other5.other.ways","degree","roleL2.degree","study.year","prof","L2.VCE","uni1.year","Context")
>
> dat_fac_demo <- all_complete_basic[,c(demographics_var,factors)]
> table(dat_fac_demo$Age) ##
## 18-25 26-30 31-35
## 318 4 1> table(dat_fac_demo$L1) # to be changed##
## Afrikaans Albanian Cantonese
## 1 1 2
## Chinese Dutch English
## 4 1 147
## English and Dutch German German and English
## 2 63 2
## I Indonesian Italian
## 1 1 87
## Japanese Mandarin Persian (Farsi)
## 1 2 1
## Romanian Russian Sindhi
## 1 2 1
## Spanish Turkish Ukrainian
## 1 1 1> table(dat_fac_demo$speak.other.L2) # to be changed##
## Afrikaans
## 1
## Arabic
## 1
## Azeri, Turkish and English
## 1
## Chinese
## 1
## Chinese (Cantonese)
## 1
## Dutch (and German)
## 1
## English
## 5
## English\nJapanese
## 1
## English, Javanese
## 1
## English, Mandarin
## 1
## French
## 10
## French and learning Spanish
## 1
## French, Greek.
## 1
## French, not fluent, but have a fair amount of knowledge
## 1
## French.
## 1
## German
## 1
## German (proficient/advanced level)
## 1
## German and Japanese
## 1
## German, French
## 1
## German.
## 1
## Greek
## 2
## Hindi
## 1
## Indonesian
## 1
## Indonesian and French (not fluent in either, but did VCE both of them)
## 1
## Irish
## 1
## Italian
## 5
## Japanese
## 1
## Macedonian
## 1
## No
## 141
## Only German
## 1
## Polish
## 1
## Russian
## 1
## Serbian
## 1
## Some Italian
## 1
## Spanish
## 2
## Swedish
## 1
## Telugu
## 1
## Urdu, English
## 1
## Yes
## 124> table(dat_fac_demo$study.other.L2) # to be changed##
## Ancient Greek
## 1
## Arab
## 1
## Arabian
## 1
## Arabic
## 2
## Arabo
## 1
## Chinese
## 7
## CHINESE
## 1
## Chinese (Mandarin)
## 1
## Chinese mandarin
## 1
## Chinese.
## 1
## Dutch
## 2
## Finnish
## 1
## Francese
## 1
## French
## 19
## French (5 semesters) until 07/2016
## 1
## french and spanish
## 2
## French and Spanish
## 1
## French, Greek.
## 1
## French,Spanish
## 1
## French.
## 2
## German
## 9
## I also study French at university level.
## 1
## I am also learning Spanish at University, although only at beginner level, unlike German.
## 1
## I study Russian, I plan to learn Spanish and hopefully also Portuguese
## 1
## I'm studying German. Since I've never studied it before, I a beginner.
## 1
## Italian
## 5
## japanese
## 1
## Japanese
## 13
## Japanese (very beginner)
## 1
## Japanese but I'm not going to continue learning it
## 1
## Japanese.
## 1
## Latin
## 1
## Latin, Ancient Egyptian, Ancient Greek.
## 1
## Latin, doesn't really count.
## 1
## Netherlands
## 1
## No
## 179
## Nonna da Pescara
## 1
## Polish
## 2
## Portoghese
## 1
## Portugese
## 1
## Portuguese
## 2
## Russian
## 15
## Russian.
## 1
## russo
## 1
## Russo
## 2
## siehe oben
## 1
## Spagnolo
## 2
## Spagnolo e un po' di giapponese
## 1
## Spanish
## 16
## Spanish (1)
## 1
## Spanish.
## 2
## tedesco
## 1
## Tedesco
## 5
## Will start my French diploma soon
## 1> table(dat_fac_demo$origins)##
## No Yes
## 254 69> table(dat_fac_demo$year.studyL2) # to be changed##
## 0 years 1- 3 years
## 33 9
## 1-3 years 4-6 years
## 7 53
## BILINGUAL First year of primary school
## 4 73
## FIRST.YEAR.SECONDARY FOURTH.YEAR.PRIMARY
## 10 5
## Kindergarten Less than a year
## 29 18
## LOWER.SECONDARY more than 6 years
## 4 41
## PERSONAL SECOND.YEAR.PRIMARY
## 2 2
## SECOND.YEAR.SECONDARY THIRD.YEAR.PRIMARY
## 2 28> table(dat_fac_demo$other5.other.ways) # to be changed##
## COURSE COURSE; MEDIA
## 5 1
## EXCHANGE FRIENDS
## 6 4
## HERITAGE L2.RESOURCES
## 4 19
## L2.RESOURCES, MEDIA L2.RESOURCES; MEDIA
## 1 1
## LIVING.L2 M.PRIVATE.LESSONS
## 7 2
## NO.OTHER.WAYS ONLINE.COURSE
## 1 4
## PERSONAL PERSONAL.HERITAGE
## 3 2
## QC SELF.STUDY
## 1 7
## STUDY.HOLIDAY TRAVEL.FRIENDS.L2.RESOURCES
## 5 1
## TRAVELLING WORK.L2
## 8 2> table(dat_fac_demo$degree) # to be changed##
## BA in Anglistik BA in Nordamerikastudien
## 39 4
## HUM HUM.SCI
## 97 5
## LA Lingue e letterature straniere
## 27 78
## Lingue, mercati e culture dell'Asia QC
## 13 4
## SCI
## 56> table(dat_fac_demo$roleL2.degree) # not usable##
## First or second language
## 91> table(dat_fac_demo$study.year)##
## 1st semester 1st year
## 70 253> table(dat_fac_demo$prof)##
## Advanced Elementary Intermediate
## 65 63 68
## Upper-intermediate
## 127> table(dat_fac_demo$L2.VCE)##
## No Yes
## 47 90> table(dat_fac_demo$uni1.year) # not usable##
## 1st year
## 162> demo_melt <- melt(all_complete_basic,id.vars = c("Age","Gender","origins","study.year","prof","L2.VCE","Context"),measure.vars = factors)
>
> # age
> ageStat <- demo_melt %>% group_by(Context,Age,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(Age[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> ageStat$Demo <- "Age"
> colnames(ageStat)[2] <- "levels"
> ageStat <- data.frame(ageStat)
>
> # Gender
> GenderStat <- demo_melt %>% group_by(Context,Gender,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(Gender[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> GenderStat$Demo <- "Gender"
> colnames(GenderStat)[2] <- "levels"
> GenderStat <- data.frame(GenderStat)
>
> # origins
> originsStat <- demo_melt %>% group_by(Context,origins,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(origins[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> originsStat$Demo <- "origins"
> colnames(originsStat)[2] <- "levels"
> originsStat <- data.frame(originsStat)
>
> # study.year
> study.yearStat <- demo_melt %>% group_by(Context,study.year,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(study.year[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> study.yearStat$Demo <- "Study Year"
> colnames(study.yearStat)[2] <- "levels"
> study.yearStat <- data.frame(study.yearStat)
>
> # prof
> profStat <- demo_melt %>% group_by(Context,prof,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(prof[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> profStat$Demo <- "Proficiency"
> colnames(profStat)[2] <- "levels"
> profStat$levels <- as.character(profStat$levels)
> profStat <- data.frame(profStat)
>
> # L2.VCE
> L2.VCEStat <- demo_melt %>% group_by(Context,L2.VCE,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(L2.VCE[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
> L2.VCEStat$Demo <- "L2.VCE"
> colnames(L2.VCEStat)[2] <- "levels"
> L2.VCEStat$levels <- as.character(L2.VCEStat$levels)
> L2.VCEStat <- data.frame(L2.VCEStat)
>
> ##################
> # Combine stats
> ##################
>
> combine_stat <- rbind(data.frame(L2.VCEStat),data.frame(profStat),study.yearStat,originsStat,ageStat,GenderStat)Tables
- Age
> kable(ageStat)| Context | levels | variable | meanFac | stdFac | nObs | seMean | CI95 | Demo |
|---|---|---|---|---|---|---|---|---|
| English in Germany | 18-25 | Factor1 | -0.7262823 | 1.3027239 | 70 | 0.1557053 | 0.3051824 | Age |
| English in Germany | 18-25 | Factor2 | 0.2482873 | 0.9717385 | 70 | 0.1161450 | 0.2276441 | Age |
| English in Germany | 18-25 | Factor3 | -0.3577921 | 0.6873827 | 70 | 0.0821579 | 0.1610296 | Age |
| English in Germany | 18-25 | Factor4 | 0.1984720 | 0.9209535 | 70 | 0.1100750 | 0.2157470 | Age |
| English in Germany | 18-25 | Factor5 | 0.0623044 | 0.8805362 | 70 | 0.1052442 | 0.2062786 | Age |
| English in Germany | 18-25 | Factor6 | -0.2942834 | 1.0170296 | 70 | 0.1215583 | 0.2382543 | Age |
| English in Italy | 18-25 | Factor1 | 0.2016837 | 0.7907478 | 88 | 0.0842940 | 0.1652162 | Age |
| English in Italy | 18-25 | Factor2 | 0.1733465 | 0.8921587 | 88 | 0.0951044 | 0.1864047 | Age |
| English in Italy | 18-25 | Factor3 | -0.0189443 | 0.9208604 | 88 | 0.0981640 | 0.1924015 | Age |
| English in Italy | 18-25 | Factor4 | 0.4647547 | 0.8003955 | 88 | 0.0853225 | 0.1672320 | Age |
| English in Italy | 18-25 | Factor5 | 0.3682684 | 0.8908858 | 88 | 0.0949687 | 0.1861387 | Age |
| English in Italy | 18-25 | Factor6 | 0.3807196 | 0.7925049 | 88 | 0.0844813 | 0.1655834 | Age |
| English in Italy | 26-30 | Factor1 | -0.3228869 | 0.8281382 | 3 | 0.4781258 | 0.9371266 | Age |
| English in Italy | 26-30 | Factor2 | -1.1240050 | 0.6045249 | 3 | 0.3490226 | 0.6840843 | Age |
| English in Italy | 26-30 | Factor3 | 0.9552972 | 0.7078003 | 3 | 0.4086487 | 0.8009514 | Age |
| English in Italy | 26-30 | Factor4 | 0.3386845 | 1.0538466 | 3 | 0.6084386 | 1.1925397 | Age |
| English in Italy | 26-30 | Factor5 | 0.1102569 | 0.8342309 | 3 | 0.4816434 | 0.9440211 | Age |
| English in Italy | 26-30 | Factor6 | -0.1919619 | 1.4612657 | 3 | 0.8436621 | 1.6535778 | Age |
| German in Australia | 18-25 | Factor1 | 0.2020605 | 0.8150940 | 86 | 0.0878938 | 0.1722719 | Age |
| German in Australia | 18-25 | Factor2 | -0.3188197 | 1.1373302 | 86 | 0.1226415 | 0.2403773 | Age |
| German in Australia | 18-25 | Factor3 | -0.0373889 | 1.0539545 | 86 | 0.1136508 | 0.2227556 | Age |
| German in Australia | 18-25 | Factor4 | -0.1423230 | 1.0223325 | 86 | 0.1102409 | 0.2160722 | Age |
| German in Australia | 18-25 | Factor5 | -0.2857307 | 1.0434491 | 86 | 0.1125180 | 0.2205353 | Age |
| German in Australia | 18-25 | Factor6 | -0.0626452 | 1.0228174 | 86 | 0.1102932 | 0.2161747 | Age |
| German in Australia | 26-30 | Factor1 | 0.7088825 | NA | 1 | NA | NA | Age |
| German in Australia | 26-30 | Factor2 | 0.4135484 | NA | 1 | NA | NA | Age |
| German in Australia | 26-30 | Factor3 | 0.3172609 | NA | 1 | NA | NA | Age |
| German in Australia | 26-30 | Factor4 | -1.4497917 | NA | 1 | NA | NA | Age |
| German in Australia | 26-30 | Factor5 | 0.0602128 | NA | 1 | NA | NA | Age |
| German in Australia | 26-30 | Factor6 | 0.2146224 | NA | 1 | NA | NA | Age |
| German in Australia | 31-35 | Factor1 | 0.1469859 | NA | 1 | NA | NA | Age |
| German in Australia | 31-35 | Factor2 | 0.5944318 | NA | 1 | NA | NA | Age |
| German in Australia | 31-35 | Factor3 | 3.5432990 | NA | 1 | NA | NA | Age |
| German in Australia | 31-35 | Factor4 | 1.0383808 | NA | 1 | NA | NA | Age |
| German in Australia | 31-35 | Factor5 | -1.1420454 | NA | 1 | NA | NA | Age |
| German in Australia | 31-35 | Factor6 | -0.4025661 | NA | 1 | NA | NA | Age |
| Italian in Australia | 18-25 | Factor1 | 0.2138808 | 0.7800659 | 74 | 0.0906808 | 0.1777344 | Age |
| Italian in Australia | 18-25 | Factor2 | -0.0385415 | 0.8820267 | 74 | 0.1025335 | 0.2009657 | Age |
| Italian in Australia | 18-25 | Factor3 | 0.3135343 | 1.1005654 | 74 | 0.1279381 | 0.2507587 | Age |
| Italian in Australia | 18-25 | Factor4 | -0.5831934 | 0.9398504 | 74 | 0.1092554 | 0.2141406 | Age |
| Italian in Australia | 18-25 | Factor5 | -0.1546625 | 1.0673902 | 74 | 0.1240816 | 0.2431999 | Age |
| Italian in Australia | 18-25 | Factor6 | -0.0912455 | 1.0643970 | 74 | 0.1237336 | 0.2425179 | Age |
- Gender
> kable(GenderStat)| Context | levels | variable | meanFac | stdFac | nObs | seMean | CI95 | Demo |
|---|---|---|---|---|---|---|---|---|
| English in Germany | Female | Factor1 | -0.5362091 | 0.9506536 | 52 | 0.1318319 | 0.2583906 | Gender |
| English in Germany | Female | Factor2 | 0.3885412 | 0.9495774 | 52 | 0.1316827 | 0.2580981 | Gender |
| English in Germany | Female | Factor3 | -0.3265721 | 0.6505681 | 52 | 0.0902176 | 0.1768264 | Gender |
| English in Germany | Female | Factor4 | 0.1971273 | 0.9761426 | 52 | 0.1353666 | 0.2653186 | Gender |
| English in Germany | Female | Factor5 | 0.0879786 | 0.9089820 | 52 | 0.1260531 | 0.2470641 | Gender |
| English in Germany | Female | Factor6 | -0.3070405 | 1.0186516 | 52 | 0.1412616 | 0.2768727 | Gender |
| English in Germany | Male | Factor1 | -1.3886141 | 1.9329243 | 17 | 0.4688030 | 0.9188539 | Gender |
| English in Germany | Male | Factor2 | -0.1434858 | 0.9726101 | 17 | 0.2358926 | 0.4623495 | Gender |
| English in Germany | Male | Factor3 | -0.4602967 | 0.8205742 | 17 | 0.1990185 | 0.3900762 | Gender |
| English in Germany | Male | Factor4 | 0.1391027 | 0.7374984 | 17 | 0.1788696 | 0.3505845 | Gender |
| English in Germany | Male | Factor5 | 0.0077952 | 0.8332351 | 17 | 0.2020892 | 0.3960948 | Gender |
| English in Germany | Male | Factor6 | -0.2204432 | 1.0606124 | 17 | 0.2572363 | 0.5041832 | Gender |
| English in Germany | Other | Factor1 | 0.6495519 | NA | 1 | NA | NA | Gender |
| English in Germany | Other | Factor2 | -0.3847753 | NA | 1 | NA | NA | Gender |
| English in Germany | Other | Factor3 | -0.2386563 | NA | 1 | NA | NA | Gender |
| English in Germany | Other | Factor4 | 1.2776709 | NA | 1 | NA | NA | Gender |
| English in Germany | Other | Factor5 | -0.3460995 | NA | 1 | NA | NA | Gender |
| English in Germany | Other | Factor6 | -0.8862000 | NA | 1 | NA | NA | Gender |
| English in Italy | Female | Factor1 | 0.2445059 | 0.7870041 | 76 | 0.0902756 | 0.1769401 | Gender |
| English in Italy | Female | Factor2 | 0.2658607 | 0.8776560 | 76 | 0.1006740 | 0.1973211 | Gender |
| English in Italy | Female | Factor3 | -0.0386774 | 0.9246114 | 76 | 0.1060602 | 0.2078780 | Gender |
| English in Italy | Female | Factor4 | 0.4884191 | 0.7652368 | 76 | 0.0877787 | 0.1720462 | Gender |
| English in Italy | Female | Factor5 | 0.4853665 | 0.8394453 | 76 | 0.0962910 | 0.1887303 | Gender |
| English in Italy | Female | Factor6 | 0.4286893 | 0.7678898 | 76 | 0.0880830 | 0.1726427 | Gender |
| English in Italy | Male | Factor1 | -0.1749122 | 0.7756565 | 14 | 0.2073029 | 0.4063137 | Gender |
| English in Italy | Male | Factor2 | -0.6067889 | 0.7886515 | 14 | 0.2107760 | 0.4131209 | Gender |
| English in Italy | Male | Factor3 | 0.3207375 | 0.9507123 | 14 | 0.2540886 | 0.4980136 | Gender |
| English in Italy | Male | Factor4 | 0.2793852 | 1.0147432 | 14 | 0.2712015 | 0.5315550 | Gender |
| English in Italy | Male | Factor5 | -0.3619614 | 0.8312086 | 14 | 0.2221498 | 0.4354137 | Gender |
| English in Italy | Male | Factor6 | 0.0166824 | 1.0217002 | 14 | 0.2730609 | 0.5351993 | Gender |
| English in Italy | Other | Factor1 | 0.6458283 | NA | 1 | NA | NA | Gender |
| English in Italy | Other | Factor2 | 0.1721009 | NA | 1 | NA | NA | Gender |
| English in Italy | Other | Factor3 | -0.3520481 | NA | 1 | NA | NA | Gender |
| English in Italy | Other | Factor4 | 0.8832194 | NA | 1 | NA | NA | Gender |
| English in Italy | Other | Factor5 | 0.9179981 | NA | 1 | NA | NA | Gender |
| English in Italy | Other | Factor6 | 0.1134957 | NA | 1 | NA | NA | Gender |
| German in Australia | Female | Factor1 | 0.2241872 | 0.8236647 | 62 | 0.1046055 | 0.2050268 | Gender |
| German in Australia | Female | Factor2 | -0.2419778 | 1.1414793 | 62 | 0.1449680 | 0.2841373 | Gender |
| German in Australia | Female | Factor3 | 0.1065525 | 1.0996852 | 62 | 0.1396602 | 0.2737339 | Gender |
| German in Australia | Female | Factor4 | -0.1498346 | 0.9968984 | 62 | 0.1266062 | 0.2481482 | Gender |
| German in Australia | Female | Factor5 | -0.2652726 | 0.9827909 | 62 | 0.1248146 | 0.2446366 | Gender |
| German in Australia | Female | Factor6 | -0.0382503 | 1.0831762 | 62 | 0.1375635 | 0.2696245 | Gender |
| German in Australia | Male | Factor1 | 0.1410805 | 0.7868610 | 25 | 0.1573722 | 0.3084495 | Gender |
| German in Australia | Male | Factor2 | -0.4349087 | 1.1381326 | 25 | 0.2276265 | 0.4461480 | Gender |
| German in Australia | Male | Factor3 | -0.2399875 | 1.1416826 | 25 | 0.2283365 | 0.4475396 | Gender |
| German in Australia | Male | Factor4 | -0.1031694 | 1.1341107 | 25 | 0.2268221 | 0.4445714 | Gender |
| German in Australia | Male | Factor5 | -0.4155953 | 1.1525169 | 25 | 0.2305034 | 0.4517866 | Gender |
| German in Australia | Male | Factor6 | -0.1423272 | 0.8459872 | 25 | 0.1691974 | 0.3316270 | Gender |
| German in Australia | Other | Factor1 | 0.8064543 | NA | 1 | NA | NA | Gender |
| German in Australia | Other | Factor2 | -0.5351778 | NA | 1 | NA | NA | Gender |
| German in Australia | Other | Factor3 | 0.0385456 | NA | 1 | NA | NA | Gender |
| German in Australia | Other | Factor4 | -0.7822120 | NA | 1 | NA | NA | Gender |
| German in Australia | Other | Factor5 | 1.1821091 | NA | 1 | NA | NA | Gender |
| German in Australia | Other | Factor6 | 0.3542736 | NA | 1 | NA | NA | Gender |
| Italian in Australia | Female | Factor1 | 0.3461925 | 0.7042137 | 58 | 0.0924678 | 0.1812369 | Gender |
| Italian in Australia | Female | Factor2 | 0.0166506 | 0.8949929 | 58 | 0.1175183 | 0.2303359 | Gender |
| Italian in Australia | Female | Factor3 | 0.2518391 | 1.1203277 | 58 | 0.1471062 | 0.2883282 | Gender |
| Italian in Australia | Female | Factor4 | -0.5622622 | 0.9602650 | 58 | 0.1260890 | 0.2471344 | Gender |
| Italian in Australia | Female | Factor5 | -0.1354413 | 1.0366148 | 58 | 0.1361142 | 0.2667838 | Gender |
| Italian in Australia | Female | Factor6 | -0.0310603 | 1.1208866 | 58 | 0.1471796 | 0.2884721 | Gender |
| Italian in Australia | Male | Factor1 | -0.3331591 | 0.8605130 | 15 | 0.2221835 | 0.4354797 | Gender |
| Italian in Australia | Male | Factor2 | -0.2722498 | 0.8470749 | 15 | 0.2187138 | 0.4286790 | Gender |
| Italian in Australia | Male | Factor3 | 0.5824455 | 1.0476294 | 15 | 0.2704967 | 0.5301736 | Gender |
| Italian in Australia | Male | Factor4 | -0.7219631 | 0.8805514 | 15 | 0.2273574 | 0.4456205 | Gender |
| Italian in Australia | Male | Factor5 | -0.3113090 | 1.1953954 | 15 | 0.3086498 | 0.6049535 | Gender |
| Italian in Australia | Male | Factor6 | -0.3228383 | 0.8483489 | 15 | 0.2190428 | 0.4293238 | Gender |
| Italian in Australia | Other | Factor1 | 0.7454041 | NA | 1 | NA | NA | Gender |
| Italian in Australia | Other | Factor2 | 0.2659421 | NA | 1 | NA | NA | Gender |
| Italian in Australia | Other | Factor3 | -0.1418149 | NA | 1 | NA | NA | Gender |
| Italian in Australia | Other | Factor4 | 0.2843367 | NA | 1 | NA | NA | Gender |
| Italian in Australia | Other | Factor5 | 1.0802064 | NA | 1 | NA | NA | Gender |
| Italian in Australia | Other | Factor6 | -0.1081004 | NA | 1 | NA | NA | Gender |
- origins
> kable(originsStat)| Context | levels | variable | meanFac | stdFac | nObs | seMean | CI95 | Demo |
|---|---|---|---|---|---|---|---|---|
| English in Germany | No | Factor1 | -0.7641797 | 1.3347397 | 65 | 0.1655541 | 0.3244860 | origins |
| English in Germany | No | Factor2 | 0.2030515 | 0.9608161 | 65 | 0.1191746 | 0.2335822 | origins |
| English in Germany | No | Factor3 | -0.3559149 | 0.6779653 | 65 | 0.0840912 | 0.1648188 | origins |
| English in Germany | No | Factor4 | 0.1848458 | 0.9404254 | 65 | 0.1166454 | 0.2286250 | origins |
| English in Germany | No | Factor5 | 0.0525202 | 0.9059865 | 65 | 0.1123738 | 0.2202526 | origins |
| English in Germany | No | Factor6 | -0.2719318 | 0.9952969 | 65 | 0.1234514 | 0.2419647 | origins |
| English in Germany | Yes | Factor1 | -0.2336162 | 0.6660137 | 5 | 0.2978504 | 0.5837868 | origins |
| English in Germany | Yes | Factor2 | 0.8363532 | 1.0259362 | 5 | 0.4588126 | 0.8992727 | origins |
| English in Germany | Yes | Factor3 | -0.3821961 | 0.8919334 | 5 | 0.3988847 | 0.7818141 | origins |
| English in Germany | Yes | Factor4 | 0.3756119 | 0.6618465 | 5 | 0.2959868 | 0.5801341 | origins |
| English in Germany | Yes | Factor5 | 0.1894985 | 0.4689573 | 5 | 0.2097241 | 0.4110592 | origins |
| English in Germany | Yes | Factor6 | -0.5848553 | 1.3707708 | 5 | 0.6130273 | 1.2015336 | origins |
| English in Italy | No | Factor1 | 0.1752930 | 0.7924622 | 90 | 0.0835328 | 0.1637244 | origins |
| English in Italy | No | Factor2 | 0.1245385 | 0.9153044 | 90 | 0.0964816 | 0.1891038 | origins |
| English in Italy | No | Factor3 | -0.0070936 | 0.9128669 | 90 | 0.0962246 | 0.1886003 | origins |
| English in Italy | No | Factor4 | 0.4574882 | 0.8067352 | 90 | 0.0850374 | 0.1666732 | origins |
| English in Italy | No | Factor5 | 0.3499917 | 0.8859265 | 90 | 0.0933849 | 0.1830343 | origins |
| English in Italy | No | Factor6 | 0.3695969 | 0.8167545 | 90 | 0.0860935 | 0.1687432 | origins |
| English in Italy | Yes | Factor1 | 1.0031425 | NA | 1 | NA | NA | origins |
| English in Italy | Yes | Factor2 | 0.6740042 | NA | 1 | NA | NA | origins |
| English in Italy | Yes | Factor3 | 1.8372190 | NA | 1 | NA | NA | origins |
| English in Italy | Yes | Factor4 | 0.7405249 | NA | 1 | NA | NA | origins |
| English in Italy | Yes | Factor5 | 1.2391415 | NA | 1 | NA | NA | origins |
| English in Italy | Yes | Factor6 | -0.3362813 | NA | 1 | NA | NA | origins |
| German in Australia | No | Factor1 | 0.2846409 | 0.7535143 | 63 | 0.0949339 | 0.1860704 | origins |
| German in Australia | No | Factor2 | -0.5158690 | 1.1610288 | 63 | 0.1462759 | 0.2867007 | origins |
| German in Australia | No | Factor3 | -0.1531695 | 0.9850181 | 63 | 0.1241006 | 0.2432372 | origins |
| German in Australia | No | Factor4 | -0.2615011 | 1.0403479 | 63 | 0.1310715 | 0.2569002 | origins |
| German in Australia | No | Factor5 | -0.2378490 | 0.9295687 | 63 | 0.1171146 | 0.2295447 | origins |
| German in Australia | No | Factor6 | -0.2209977 | 0.9783657 | 63 | 0.1232625 | 0.2415945 | origins |
| German in Australia | Yes | Factor1 | 0.0120280 | 0.9173562 | 25 | 0.1834712 | 0.3596036 | origins |
| German in Australia | Yes | Factor2 | 0.2435693 | 0.8512520 | 25 | 0.1702504 | 0.3336908 | origins |
| German in Australia | Yes | Factor3 | 0.4117916 | 1.3121556 | 25 | 0.2624311 | 0.5143650 | origins |
| German in Australia | Yes | Factor4 | 0.1529352 | 0.9522807 | 25 | 0.1904561 | 0.3732940 | origins |
| German in Australia | Yes | Factor5 | -0.4268073 | 1.2777535 | 25 | 0.2555507 | 0.5008794 | origins |
| German in Australia | Yes | Factor6 | 0.3338972 | 1.0053682 | 25 | 0.2010736 | 0.3941043 | origins |
| Italian in Australia | No | Factor1 | 0.1388624 | 0.8274762 | 36 | 0.1379127 | 0.2703089 | origins |
| Italian in Australia | No | Factor2 | -0.1859431 | 0.9591491 | 36 | 0.1598582 | 0.3133220 | origins |
| Italian in Australia | No | Factor3 | 0.1082674 | 1.0885224 | 36 | 0.1814204 | 0.3555840 | origins |
| Italian in Australia | No | Factor4 | -0.5638098 | 0.9150401 | 36 | 0.1525067 | 0.2989131 | origins |
| Italian in Australia | No | Factor5 | -0.0754663 | 1.0707548 | 36 | 0.1784591 | 0.3497799 | origins |
| Italian in Australia | No | Factor6 | 0.1446808 | 0.9866241 | 36 | 0.1644374 | 0.3222972 | origins |
| Italian in Australia | Yes | Factor1 | 0.2849508 | 0.7363363 | 38 | 0.1194495 | 0.2341211 | origins |
| Italian in Australia | Yes | Factor2 | 0.1011022 | 0.7896262 | 38 | 0.1280943 | 0.2510648 | origins |
| Italian in Australia | Yes | Factor3 | 0.5079976 | 1.0904518 | 38 | 0.1768946 | 0.3467135 | origins |
| Italian in Australia | Yes | Factor4 | -0.6015569 | 0.9746862 | 38 | 0.1581150 | 0.3099054 | origins |
| Italian in Australia | Yes | Factor5 | -0.2296905 | 1.0730449 | 38 | 0.1740709 | 0.3411789 | origins |
| Italian in Australia | Yes | Factor6 | -0.3147547 | 1.0995405 | 38 | 0.1783690 | 0.3496033 | origins |
- study.year
> kable(study.yearStat)| Context | levels | variable | meanFac | stdFac | nObs | seMean | CI95 | Demo |
|---|---|---|---|---|---|---|---|---|
| English in Germany | 1st semester | Factor1 | -0.7262823 | 1.3027239 | 70 | 0.1557053 | 0.3051824 | Study Year |
| English in Germany | 1st semester | Factor2 | 0.2482873 | 0.9717385 | 70 | 0.1161450 | 0.2276441 | Study Year |
| English in Germany | 1st semester | Factor3 | -0.3577921 | 0.6873827 | 70 | 0.0821579 | 0.1610296 | Study Year |
| English in Germany | 1st semester | Factor4 | 0.1984720 | 0.9209535 | 70 | 0.1100750 | 0.2157470 | Study Year |
| English in Germany | 1st semester | Factor5 | 0.0623044 | 0.8805362 | 70 | 0.1052442 | 0.2062786 | Study Year |
| English in Germany | 1st semester | Factor6 | -0.2942834 | 1.0170296 | 70 | 0.1215583 | 0.2382543 | Study Year |
| English in Italy | 1st year | Factor1 | 0.1843902 | 0.7928113 | 91 | 0.0831092 | 0.1628940 | Study Year |
| English in Italy | 1st year | Factor2 | 0.1305766 | 0.9120258 | 91 | 0.0956063 | 0.1873883 | Study Year |
| English in Italy | 1st year | Factor3 | 0.0131736 | 0.9281411 | 91 | 0.0972956 | 0.1906994 | Study Year |
| English in Italy | 1st year | Factor4 | 0.4605985 | 0.8027893 | 91 | 0.0841552 | 0.1649442 | Study Year |
| English in Italy | 1st year | Factor5 | 0.3597625 | 0.8859079 | 91 | 0.0928684 | 0.1820220 | Study Year |
| English in Italy | 1st year | Factor6 | 0.3618400 | 0.8155681 | 91 | 0.0854948 | 0.1675697 | Study Year |
| German in Australia | 1st year | Factor1 | 0.2071940 | 0.8075060 | 88 | 0.0860804 | 0.1687176 | Study Year |
| German in Australia | 1st year | Factor2 | -0.3001195 | 1.1310092 | 88 | 0.1205660 | 0.2363093 | Study Year |
| German in Australia | 1st year | Factor3 | 0.0073308 | 1.1099903 | 88 | 0.1183254 | 0.2319177 | Study Year |
| German in Australia | 1st year | Factor4 | -0.1437635 | 1.0280115 | 88 | 0.1095864 | 0.2147893 | Study Year |
| German in Australia | 1st year | Factor5 | -0.2915304 | 1.0361113 | 88 | 0.1104498 | 0.2164817 | Study Year |
| German in Australia | 1st year | Factor6 | -0.0633571 | 1.0120855 | 88 | 0.1078887 | 0.2114618 | Study Year |
| Italian in Australia | 1st year | Factor1 | 0.2138808 | 0.7800659 | 74 | 0.0906808 | 0.1777344 | Study Year |
| Italian in Australia | 1st year | Factor2 | -0.0385415 | 0.8820267 | 74 | 0.1025335 | 0.2009657 | Study Year |
| Italian in Australia | 1st year | Factor3 | 0.3135343 | 1.1005654 | 74 | 0.1279381 | 0.2507587 | Study Year |
| Italian in Australia | 1st year | Factor4 | -0.5831934 | 0.9398504 | 74 | 0.1092554 | 0.2141406 | Study Year |
| Italian in Australia | 1st year | Factor5 | -0.1546625 | 1.0673902 | 74 | 0.1240816 | 0.2431999 | Study Year |
| Italian in Australia | 1st year | Factor6 | -0.0912455 | 1.0643970 | 74 | 0.1237336 | 0.2425179 | Study Year |
- prof
> kable(profStat)| Context | levels | variable | meanFac | stdFac | nObs | seMean | CI95 | Demo |
|---|---|---|---|---|---|---|---|---|
| English in Germany | Advanced | Factor1 | -0.9271504 | 1.5300313 | 38 | 0.2482038 | 0.4864795 | Proficiency |
| English in Germany | Advanced | Factor2 | 0.4634883 | 0.9158941 | 38 | 0.1485776 | 0.2912122 | Proficiency |
| English in Germany | Advanced | Factor3 | -0.4154023 | 0.6556700 | 38 | 0.1063637 | 0.2084729 | Proficiency |
| English in Germany | Advanced | Factor4 | 0.3568224 | 0.8894604 | 38 | 0.1442895 | 0.2828075 | Proficiency |
| English in Germany | Advanced | Factor5 | 0.2377727 | 0.7958279 | 38 | 0.1291003 | 0.2530366 | Proficiency |
| English in Germany | Advanced | Factor6 | -0.3911355 | 1.0762927 | 38 | 0.1745977 | 0.3422116 | Proficiency |
| English in Germany | Intermediate | Factor1 | -0.4667816 | 1.1901341 | 5 | 0.5322441 | 1.0431985 | Proficiency |
| English in Germany | Intermediate | Factor2 | 0.3362592 | 1.2471170 | 5 | 0.5577277 | 1.0931462 | Proficiency |
| English in Germany | Intermediate | Factor3 | -0.3855761 | 0.3870876 | 5 | 0.1731108 | 0.3392973 | Proficiency |
| English in Germany | Intermediate | Factor4 | 0.1894429 | 0.5555855 | 5 | 0.2484654 | 0.4869922 | Proficiency |
| English in Germany | Intermediate | Factor5 | -0.4103536 | 0.7856321 | 5 | 0.3513453 | 0.6886369 | Proficiency |
| English in Germany | Intermediate | Factor6 | 0.3245462 | 0.5215715 | 5 | 0.2332539 | 0.4571775 | Proficiency |
| English in Germany | Upper-intermediate | Factor1 | -0.4916348 | 0.9085096 | 27 | 0.1748428 | 0.3426918 | Proficiency |
| English in Germany | Upper-intermediate | Factor2 | -0.0708793 | 0.9476078 | 27 | 0.1823672 | 0.3574397 | Proficiency |
| English in Germany | Upper-intermediate | Factor3 | -0.2715659 | 0.7786987 | 27 | 0.1498606 | 0.2937268 | Proficiency |
| English in Germany | Upper-intermediate | Factor4 | -0.0227196 | 0.9950183 | 27 | 0.1914914 | 0.3753231 | Proficiency |
| English in Germany | Upper-intermediate | Factor5 | -0.0971217 | 0.9731579 | 27 | 0.1872843 | 0.3670773 | Proficiency |
| English in Germany | Upper-intermediate | Factor6 | -0.2725712 | 0.9832638 | 27 | 0.1892292 | 0.3708892 | Proficiency |
| English in Italy | Advanced | Factor1 | 0.1112143 | 0.7828444 | 23 | 0.1632343 | 0.3199393 | Proficiency |
| English in Italy | Advanced | Factor2 | 0.2517485 | 0.7725476 | 23 | 0.1610873 | 0.3157311 | Proficiency |
| English in Italy | Advanced | Factor3 | -0.2283676 | 0.7054717 | 23 | 0.1471010 | 0.2883180 | Proficiency |
| English in Italy | Advanced | Factor4 | 0.3912780 | 0.9927381 | 23 | 0.2070002 | 0.4057204 | Proficiency |
| English in Italy | Advanced | Factor5 | 0.7124470 | 0.8144011 | 23 | 0.1698144 | 0.3328362 | Proficiency |
| English in Italy | Advanced | Factor6 | 0.1026296 | 1.0182434 | 23 | 0.2123184 | 0.4161441 | Proficiency |
| English in Italy | Elementary | Factor1 | 0.7732101 | 0.2811153 | 2 | 0.1987786 | 0.3896060 | Proficiency |
| English in Italy | Elementary | Factor2 | -1.5460078 | 0.1255888 | 2 | 0.0888047 | 0.1740572 | Proficiency |
| English in Italy | Elementary | Factor3 | -1.0334221 | 0.0051180 | 2 | 0.0036190 | 0.0070932 | Proficiency |
| English in Italy | Elementary | Factor4 | -0.3606474 | 0.2408275 | 2 | 0.1702908 | 0.3337699 | Proficiency |
| English in Italy | Elementary | Factor5 | -0.7182939 | 0.4631056 | 2 | 0.3274651 | 0.6418316 | Proficiency |
| English in Italy | Elementary | Factor6 | -0.0025834 | 0.7284323 | 2 | 0.5150794 | 1.0095556 | Proficiency |
| English in Italy | Intermediate | Factor1 | -0.0699550 | 0.9538531 | 9 | 0.3179510 | 0.6231840 | Proficiency |
| English in Italy | Intermediate | Factor2 | -0.0196820 | 0.7483122 | 9 | 0.2494374 | 0.4888973 | Proficiency |
| English in Italy | Intermediate | Factor3 | -0.0252492 | 1.1173725 | 9 | 0.3724575 | 0.7300167 | Proficiency |
| English in Italy | Intermediate | Factor4 | 0.2818654 | 0.8158344 | 9 | 0.2719448 | 0.5330118 | Proficiency |
| English in Italy | Intermediate | Factor5 | 0.0508466 | 0.9254897 | 9 | 0.3084966 | 0.6046533 | Proficiency |
| English in Italy | Intermediate | Factor6 | 0.4979298 | 0.4077665 | 9 | 0.1359222 | 0.2664075 | Proficiency |
| English in Italy | Upper-intermediate | Factor1 | 0.2334168 | 0.7814062 | 57 | 0.1034998 | 0.2028596 | Proficiency |
| English in Italy | Upper-intermediate | Factor2 | 0.1642353 | 0.9543889 | 57 | 0.1264119 | 0.2477673 | Proficiency |
| English in Italy | Upper-intermediate | Factor3 | 0.1534270 | 0.9629346 | 57 | 0.1275438 | 0.2499859 | Proficiency |
| English in Italy | Upper-intermediate | Factor4 | 0.5456067 | 0.7168784 | 57 | 0.0949529 | 0.1861076 | Proficiency |
| English in Italy | Upper-intermediate | Factor5 | 0.3040540 | 0.8737819 | 57 | 0.1157352 | 0.2268411 | Proficiency |
| English in Italy | Upper-intermediate | Factor6 | 0.4577325 | 0.7640468 | 57 | 0.1012005 | 0.1983529 | Proficiency |
| German in Australia | Advanced | Factor1 | 0.6790965 | 0.1940384 | 4 | 0.0970192 | 0.1901576 | Proficiency |
| German in Australia | Advanced | Factor2 | 0.5489980 | 1.1002650 | 4 | 0.5501325 | 1.0782597 | Proficiency |
| German in Australia | Advanced | Factor3 | 0.5360460 | 1.7449809 | 4 | 0.8724904 | 1.7100813 | Proficiency |
| German in Australia | Advanced | Factor4 | 0.3991436 | 1.0773996 | 4 | 0.5386998 | 1.0558516 | Proficiency |
| German in Australia | Advanced | Factor5 | -0.0732584 | 1.2256152 | 4 | 0.6128076 | 1.2011029 | Proficiency |
| German in Australia | Advanced | Factor6 | -0.3060320 | 1.3490813 | 4 | 0.6745407 | 1.3220997 | Proficiency |
| German in Australia | Elementary | Factor1 | 0.1726697 | 0.8929717 | 32 | 0.1578566 | 0.3093989 | Proficiency |
| German in Australia | Elementary | Factor2 | -0.4772904 | 1.2554044 | 32 | 0.2219262 | 0.4349754 | Proficiency |
| German in Australia | Elementary | Factor3 | 0.2774221 | 1.2515834 | 32 | 0.2212508 | 0.4336515 | Proficiency |
| German in Australia | Elementary | Factor4 | -0.0126798 | 1.0346949 | 32 | 0.1829100 | 0.3585035 | Proficiency |
| German in Australia | Elementary | Factor5 | -0.4644653 | 1.1871245 | 32 | 0.2098559 | 0.4113176 | Proficiency |
| German in Australia | Elementary | Factor6 | 0.0878670 | 0.9803469 | 32 | 0.1733025 | 0.3396729 | Proficiency |
| German in Australia | Intermediate | Factor1 | 0.2848941 | 0.7657391 | 25 | 0.1531478 | 0.3001697 | Proficiency |
| German in Australia | Intermediate | Factor2 | -0.2254990 | 0.9449678 | 25 | 0.1889936 | 0.3704274 | Proficiency |
| German in Australia | Intermediate | Factor3 | -0.0297787 | 0.8122987 | 25 | 0.1624597 | 0.3184211 | Proficiency |
| German in Australia | Intermediate | Factor4 | -0.0697978 | 0.9901476 | 25 | 0.1980295 | 0.3881379 | Proficiency |
| German in Australia | Intermediate | Factor5 | -0.2234672 | 1.0112182 | 25 | 0.2022436 | 0.3963975 | Proficiency |
| German in Australia | Intermediate | Factor6 | -0.0571849 | 0.9571603 | 25 | 0.1914321 | 0.3752068 | Proficiency |
| German in Australia | Upper-intermediate | Factor1 | 0.1062558 | 0.7959016 | 27 | 0.1531713 | 0.3002158 | Proficiency |
| German in Australia | Upper-intermediate | Factor2 | -0.2850274 | 1.1323031 | 27 | 0.2179118 | 0.4271072 | Proficiency |
| German in Australia | Upper-intermediate | Factor3 | -0.3567448 | 1.0113570 | 27 | 0.1946357 | 0.3814861 | Proficiency |
| German in Australia | Upper-intermediate | Factor4 | -0.4480394 | 1.0261334 | 27 | 0.1974795 | 0.3870598 | Proficiency |
| German in Australia | Upper-intermediate | Factor5 | -0.1819285 | 0.8565132 | 27 | 0.1648360 | 0.3230786 | Proficiency |
| German in Australia | Upper-intermediate | Factor6 | -0.2123490 | 1.0796478 | 27 | 0.2077783 | 0.4072455 | Proficiency |
| Italian in Australia | Elementary | Factor1 | 0.3988607 | 0.5915718 | 29 | 0.1098521 | 0.2153102 | Proficiency |
| Italian in Australia | Elementary | Factor2 | -0.0708709 | 0.9238497 | 29 | 0.1715546 | 0.3362470 | Proficiency |
| Italian in Australia | Elementary | Factor3 | 0.0665344 | 1.1168361 | 29 | 0.2073913 | 0.4064869 | Proficiency |
| Italian in Australia | Elementary | Factor4 | -0.7434267 | 0.9213038 | 29 | 0.1710818 | 0.3353204 | Proficiency |
| Italian in Australia | Elementary | Factor5 | -0.1387209 | 0.9771702 | 29 | 0.1814560 | 0.3556537 | Proficiency |
| Italian in Australia | Elementary | Factor6 | -0.3355611 | 1.1952673 | 29 | 0.2219556 | 0.4350329 | Proficiency |
| Italian in Australia | Intermediate | Factor1 | 0.1435846 | 0.8611611 | 29 | 0.1599136 | 0.3134307 | Proficiency |
| Italian in Australia | Intermediate | Factor2 | -0.0845104 | 0.8966208 | 29 | 0.1664983 | 0.3263367 | Proficiency |
| Italian in Australia | Intermediate | Factor3 | 0.2461656 | 1.1509025 | 29 | 0.2137172 | 0.4188858 | Proficiency |
| Italian in Australia | Intermediate | Factor4 | -0.3964917 | 0.9789891 | 29 | 0.1817937 | 0.3563157 | Proficiency |
| Italian in Australia | Intermediate | Factor5 | -0.1551437 | 1.1981171 | 29 | 0.2224848 | 0.4360701 | Proficiency |
| Italian in Australia | Intermediate | Factor6 | 0.1080487 | 0.9501073 | 29 | 0.1764305 | 0.3458038 | Proficiency |
| Italian in Australia | Upper-intermediate | Factor1 | 0.0060166 | 0.8955522 | 16 | 0.2238881 | 0.4388206 | Proficiency |
| Italian in Australia | Upper-intermediate | Factor2 | 0.1033743 | 0.8152749 | 16 | 0.2038187 | 0.3994847 | Proficiency |
| Italian in Australia | Upper-intermediate | Factor3 | 0.8833274 | 0.7879146 | 16 | 0.1969786 | 0.3860782 | Proficiency |
| Italian in Australia | Upper-intermediate | Factor4 | -0.6311676 | 0.8976950 | 16 | 0.2244238 | 0.4398706 | Proficiency |
| Italian in Australia | Upper-intermediate | Factor5 | -0.1826846 | 1.0398992 | 16 | 0.2599748 | 0.5095506 | Proficiency |
| Italian in Australia | Upper-intermediate | Factor6 | -0.0096444 | 0.9810545 | 16 | 0.2452636 | 0.4807167 | Proficiency |
- L2.VCE
> kable(L2.VCEStat)| Context | levels | variable | meanFac | stdFac | nObs | seMean | CI95 | Demo |
|---|---|---|---|---|---|---|---|---|
| English in Germany | NA | Factor1 | -0.7262823 | 1.3027239 | 70 | 0.1557053 | 0.3051824 | L2.VCE |
| English in Germany | NA | Factor2 | 0.2482873 | 0.9717385 | 70 | 0.1161450 | 0.2276441 | L2.VCE |
| English in Germany | NA | Factor3 | -0.3577921 | 0.6873827 | 70 | 0.0821579 | 0.1610296 | L2.VCE |
| English in Germany | NA | Factor4 | 0.1984720 | 0.9209535 | 70 | 0.1100750 | 0.2157470 | L2.VCE |
| English in Germany | NA | Factor5 | 0.0623044 | 0.8805362 | 70 | 0.1052442 | 0.2062786 | L2.VCE |
| English in Germany | NA | Factor6 | -0.2942834 | 1.0170296 | 70 | 0.1215583 | 0.2382543 | L2.VCE |
| English in Italy | NA | Factor1 | 0.1843902 | 0.7928113 | 91 | 0.0831092 | 0.1628940 | L2.VCE |
| English in Italy | NA | Factor2 | 0.1305766 | 0.9120258 | 91 | 0.0956063 | 0.1873883 | L2.VCE |
| English in Italy | NA | Factor3 | 0.0131736 | 0.9281411 | 91 | 0.0972956 | 0.1906994 | L2.VCE |
| English in Italy | NA | Factor4 | 0.4605985 | 0.8027893 | 91 | 0.0841552 | 0.1649442 | L2.VCE |
| English in Italy | NA | Factor5 | 0.3597625 | 0.8859079 | 91 | 0.0928684 | 0.1820220 | L2.VCE |
| English in Italy | NA | Factor6 | 0.3618400 | 0.8155681 | 91 | 0.0854948 | 0.1675697 | L2.VCE |
| German in Australia | No | Factor1 | 0.1122457 | 0.9648921 | 27 | 0.1856936 | 0.3639594 | L2.VCE |
| German in Australia | No | Factor2 | -0.2900239 | 1.3687674 | 27 | 0.2634194 | 0.5163020 | L2.VCE |
| German in Australia | No | Factor3 | 0.3839422 | 1.3227110 | 27 | 0.2545559 | 0.4989295 | L2.VCE |
| German in Australia | No | Factor4 | 0.0746106 | 1.1262506 | 27 | 0.2167470 | 0.4248242 | L2.VCE |
| German in Australia | No | Factor5 | -0.5172007 | 1.2312299 | 27 | 0.2369503 | 0.4644226 | L2.VCE |
| German in Australia | No | Factor6 | 0.1078708 | 1.1534274 | 27 | 0.2219772 | 0.4350753 | L2.VCE |
| German in Australia | Yes | Factor1 | 0.2648504 | 0.7443187 | 48 | 0.1074331 | 0.2105690 | L2.VCE |
| German in Australia | Yes | Factor2 | -0.1639023 | 0.9364911 | 48 | 0.1351709 | 0.2649349 | L2.VCE |
| German in Australia | Yes | Factor3 | -0.0768859 | 1.0292376 | 48 | 0.1485577 | 0.2911730 | L2.VCE |
| German in Australia | Yes | Factor4 | -0.2070704 | 1.0333339 | 48 | 0.1491489 | 0.2923318 | L2.VCE |
| German in Australia | Yes | Factor5 | -0.2699711 | 0.9338146 | 48 | 0.1347845 | 0.2641777 | L2.VCE |
| German in Australia | Yes | Factor6 | -0.1207312 | 0.9108348 | 48 | 0.1314677 | 0.2576767 | L2.VCE |
| German in Australia | NA | Factor1 | 0.1915092 | 0.7117258 | 13 | 0.1973972 | 0.3868986 | L2.VCE |
| German in Australia | NA | Factor2 | -0.8240430 | 1.1865783 | 13 | 0.3290976 | 0.6450313 | L2.VCE |
| German in Australia | NA | Factor3 | -0.4639077 | 0.6361848 | 13 | 0.1764459 | 0.3458340 | L2.VCE |
| German in Australia | NA | Factor4 | -0.3635613 | 0.7454995 | 13 | 0.2067644 | 0.4052581 | L2.VCE |
| German in Australia | NA | Factor5 | 0.0975662 | 0.8959518 | 13 | 0.2484923 | 0.4870450 | L2.VCE |
| German in Australia | NA | Factor6 | -0.2071416 | 1.0921995 | 13 | 0.3029216 | 0.5937264 | L2.VCE |
| Italian in Australia | No | Factor1 | 0.3350916 | 0.5653301 | 20 | 0.1264116 | 0.2477668 | L2.VCE |
| Italian in Australia | No | Factor2 | -0.2348379 | 0.9164961 | 20 | 0.2049347 | 0.4016721 | L2.VCE |
| Italian in Australia | No | Factor3 | 0.2004212 | 1.2822388 | 20 | 0.2867173 | 0.5619659 | L2.VCE |
| Italian in Australia | No | Factor4 | -0.5778701 | 0.8374376 | 20 | 0.1872567 | 0.3670232 | L2.VCE |
| Italian in Australia | No | Factor5 | -0.2866449 | 1.0547672 | 20 | 0.2358531 | 0.4622721 | L2.VCE |
| Italian in Australia | No | Factor6 | -0.4331088 | 1.2146982 | 20 | 0.2716148 | 0.5323650 | L2.VCE |
| Italian in Australia | Yes | Factor1 | 0.1356455 | 0.8735394 | 42 | 0.1347901 | 0.2641885 | L2.VCE |
| Italian in Australia | Yes | Factor2 | -0.0132805 | 0.8800518 | 42 | 0.1357949 | 0.2661581 | L2.VCE |
| Italian in Australia | Yes | Factor3 | 0.3968176 | 1.0149814 | 42 | 0.1566150 | 0.3069654 | L2.VCE |
| Italian in Australia | Yes | Factor4 | -0.4506052 | 1.0013393 | 42 | 0.1545100 | 0.3028396 | L2.VCE |
| Italian in Australia | Yes | Factor5 | -0.1475108 | 1.1523258 | 42 | 0.1778077 | 0.3485032 | L2.VCE |
| Italian in Australia | Yes | Factor6 | 0.1347609 | 0.9372639 | 42 | 0.1446230 | 0.2834610 | L2.VCE |
| Italian in Australia | NA | Factor1 | 0.2856864 | 0.7657221 | 12 | 0.2210449 | 0.4332480 | L2.VCE |
| Italian in Australia | NA | Factor2 | 0.2002060 | 0.8310431 | 12 | 0.2399015 | 0.4702069 | L2.VCE |
| Italian in Australia | NA | Factor3 | 0.2105644 | 1.1389839 | 12 | 0.3287963 | 0.6444408 | L2.VCE |
| Italian in Australia | NA | Factor4 | -1.0561245 | 0.7760488 | 12 | 0.2240260 | 0.4390909 | L2.VCE |
| Italian in Australia | NA | Factor5 | 0.0402769 | 0.7853055 | 12 | 0.2266982 | 0.4443284 | L2.VCE |
| Italian in Australia | NA | Factor6 | -0.3124960 | 1.1112320 | 12 | 0.3207850 | 0.6287387 | L2.VCE |
Factor means with Confidence Intervals
> pos <- position_dodge(width=0.4)
> ggplot(subset(combine_stat,variable %in% c("Factor1")),aes(x=levels,y=meanFac,colour=Context,group=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor1: Mean +- 95% CI") + theme_bw()## Warning: Removed 7 rows containing missing values (geom_errorbar).
> pos <- position_dodge(width=0.4)
> ggplot(subset(combine_stat,variable %in% c("Factor2")),aes(x=levels,y=meanFac,colour=Context,group=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor2: Mean +- 95% CI") + theme_bw()## Warning: Removed 7 rows containing missing values (geom_errorbar).
> pos <- position_dodge(width=0.4)
> ggplot(subset(combine_stat,variable %in% c("Factor3")),aes(x=levels,y=meanFac,colour=Context,group=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor3: Mean +- 95% CI") + theme_bw()## Warning: Removed 7 rows containing missing values (geom_errorbar).
> pos <- position_dodge(width=0.4)
> ggplot(subset(combine_stat,variable %in% c("Factor4")),aes(x=levels,y=meanFac,colour=Context,group=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor4: Mean +- 95% CI") + theme_bw()## Warning: Removed 7 rows containing missing values (geom_errorbar).
> pos <- position_dodge(width=0.4)
> ggplot(subset(combine_stat,variable %in% c("Factor5")),aes(x=levels,y=meanFac,colour=Context,group=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor5: Mean +- 95% CI") + theme_bw()## Warning: Removed 7 rows containing missing values (geom_errorbar).
> pos <- position_dodge(width=0.4)
> ggplot(subset(combine_stat,variable %in% c("Factor6")),aes(x=levels,y=meanFac,colour=Context,group=Context)) +
+ geom_errorbar(aes(ymin=meanFac-CI95, ymax=meanFac+CI95),width=0.2,position=pos) + facet_wrap(~Demo ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor6: Mean +- 95% CI") + theme_bw()## Warning: Removed 7 rows containing missing values (geom_errorbar).
Degree (HUM vs STEMM)
We could probably reduce the number of classes.
> degree_melt <- melt(all_complete_basic,id.vars = c("degree","Context"),measure.vars = factors)
>
> # degree
> degreeStat <- degree_melt %>% group_by(Context,degree,variable) %>%
+ summarise(meanFac = mean(value,na.rm=TRUE),
+ stdFac = sd(value,na.rm=TRUE),
+ nObs = length(degree[!is.na(value)])) %>%
+ mutate(seMean = stdFac/sqrt(nObs),
+ CI95 = 1.96*seMean)
>
>
> degreeStat <- data.frame(degreeStat)
>
> pos <- position_dodge(width=0.4)
> ggplot(subset(degreeStat),aes(x=variable,y=meanFac,colour=degree,group=degree)) +
+ geom_errorbar(aes(ymin=meanFac-stdFac, ymax=meanFac+stdFac),width=0.2,position=pos) + facet_wrap(~Context ,scales="free") +
+ geom_point(position=pos) + ggtitle("Factor6: Mean +- stDev") + theme_bw()
> pos <- position_dodge(width=0.4)
> ggplot(subset(degreeStat),aes(x=Context,y=meanFac,colour=degree,group=degree)) +
+ geom_errorbar(aes(ymin=meanFac-stdFac, ymax=meanFac+stdFac),width=0.2,position=pos) + facet_wrap(~variable ,scales="free") +
+ geom_point(position=pos) + ggtitle("Mean +- stDev") + theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1))